AI Implementation: Transforming Business Processes

Andrew Andreev

Author at ApiX-Drive

Reading time: ~8 min

Current Trends in the Use of AI in Business

• 56% of respondents have already completed the implementation AI in customer service and support processes. This is currently the most popular area of integration.
• A slightly smaller share of entrepreneurs (51%) use AI for security tasks, including cybersecurity and fraud management.
  
• 47% of respondents use AI in virtual assistants. 46% use it for customer relationship management (CRM). 40% use it for inventory management. 35% create content using it.
  
• These promising technologies are also in demand in product recommendation (33%), supply chain management (30%), accounting (30%), recruiting, and personnel management (26%), audience targeting (24%).
  

Advantages and Benefits

• Business process automation. AI has serious potential to automate and optimize most manufacturing and service processes. Neural networks effectively control robotic lines, test, and work with programs, accept applications and make payments, communicate with clients, and interact with human workers.
• Savings when training staff. Artificial intelligence trains employees quickly and efficiently. Thus, it helps the business to significantly reduce its costs. The skill of self-learning increases its ability to adapt.
  
• Improved user experience. By learning on its own and processing gigantic amounts of data, AI excels at customer service tasks. Smart chatbots based on neural networks can communicate with users and provide them with personalized support 24/7. With their help, companies effectively strengthen audience loyalty.
  
• New level of analytics. Artificial intelligence has proven itself well in predictive analytics. It easily copes with processing big data, finds patterns, forms logical conclusions, and makes relevant forecasts. The capabilities of neural networks to analyze volumetric data in real-time help businesses make the right decisions faster and thus outperform competitors.
  
• Improved data protection. AI solutions for business provide a high level of data security, preventing hacking attempts, information theft, or other illegal actions. This is a critical advantage for those business areas that pay increased attention to data protection (for example, financial technologies and banks).
  
• Optimization of resource consumption. The latest AI models help businesses optimize a number of important work processes and thereby achieve tangible savings on key resources. To achieve this, they use 24/7 monitoring, faster data processing, lower operating expenses and reduced downtime. Additionally, they minimize the likelihood of human errors.
  

Disadvantages and Challenges

• Insufficient volume or poor quality of data. Information is the most significant factor in successful AI integration. The performance of neural networks and the results they generate directly depend on it. Even the slightest flaws here can lead to the fact that the implemented technologies turn out to be useless or even harmful to the business. To avoid this, companies should pay increased attention to data auditing and use robust data management systems.
• Compatibility with existing systems. Implementing AI into an existing enterprise management system, security platform, or supply chain often requires a major overhaul. During such updates, it is not enough to install several plugins. Instead, it will take much more action and resources. Before launching integration, we recommend conducting a thorough audit of the infrastructure and training staff.
  
• Choice of strategy. AI business implementation does not yet have universal algorithms, so companies have to choose and test their integration strategy on their own. At the preparatory stage, evaluate the available data, infrastructure, and overall business readiness for such significant changes. Then develop a roadmap outlining the sequence of activities, required resources, and expected completion date for the project.
  
• Ethical challenges and safety issues. As businesses implement artificial intelligence, they typically face ethical challenges related to the responsible use of new technologies. Another potentially dangerous factor is data security. To address these threats, companies need to effectively protect the personal information about customers and employees, and develop ethical standards for the use of AI.
  

How to Implement AI in Business

1. Identification. First, you need to determine which divisions of your company and areas of its activity need optimization the most. Most often, these include the most labor-intensive and routine tasks, such as customer service or data processing. Additionally, AI delivers the best results in business processes that require high speed and accuracy.
2. Technologies. At this point, select the AI technologies you're interested in and research the major providers. Focus on the provider's experience, customer reviews, and the availability of solutions that suit you. Pay attention to adapting the selected technologies to your company's processes and objectives. It is worth considering their compliance with regulations, potential risks and security measures, and also calculating the costs of integration and expected profitability.
   
3. Planning. Prepare a preliminary project plan, indicating the expected costs, timing, and results of implementing AI in business process management. Describe the responsibilities of employees who will implement and use new technologies. Be sure to consider the likely risks and difficulties of implementing the project, thinking through ways to solve them.
   
4. Data. One of the most important stages of introducing AI into business is data audit. Before starting a project, you must be sure that their volume and quality will allow you to successfully implement the task and achieve the desired effect.
   
5. Team. Finding qualified talent in the field of artificial intelligence and data management is another significant step. You will need to form a team to carry out the AI integration, consisting of data analysts, engineers, and project managers. In addition to the direct implementation of technology, specialists must train your staff to properly use AI in their work.
   
6. Testing. After completing all of the above steps, be sure to test the functionality of the updated system before a full-scale launch. This will help you evaluate its performance, find and correct possible errors, and also collect feedback from employees and clients to improve the technology.
   

Examples of Artificial Intelligence in Business

Analytics

Quality control

Customer service

Conclusion